← All roles

AI Performance Engineer

Applied Intuition· Data & Compute Platform
Apply on company site
Sunnyvale$215k - $285kFull-timePosted 2mo ago

About the role

Performance engineer focused on accelerating large-scale ML workloads in datacenters, optimizing distributed training and offline batch inference to maximize throughput and minimize cost per data processed.

Responsibilities

  • Profile and optimize distributed training end to end - data loading and preprocessing, augmentation, kernel execution, gradient communication, and checkpointing
  • Optimize large-scale offline and batch inference over petabyte-scale sensor logs: batching and scheduling strategies, quantization and low-precision execution, graph optimization, and accelerator saturation across long-running sweeps
  • Establish roofline and performance models for workloads, quantify gaps between achieved and theoretical performance, and prioritize optimization opportunities
  • Improve multi-node scaling efficiency: sharding and parallelism strategies, collective communication, interconnect utilization, and memory-bandwidth and kernel-fusion bottlenecks
  • Drive cluster goodput - reduce GPU idle time from input pipeline stalls, storage and network I/O, scheduling gaps, stragglers, and failure recovery on long-running jobs
  • Build benchmarking, observability, and regression-detection tooling to prevent performance degradation as models evolve
  • Collaborate with engineers across functions to solve complex data and compute problems at scale
  • Contribute to a team culture that values collaboration, technical excellence, and innovation

Qualifications

  • Bachelor's degree or higher in computer science or adjacent fields
  • 1 or more years of hands-on experience with ML performance engineering: profiling, roofline analysis, throughput optimization, and root-cause investigation in production systems
  • 5 or more years of overall experience
  • Experience with distributed multi-node training at scale (FSDP, DeepSpeed, Megatron, NCCL, or equivalent) including diagnosing scaling inefficiency as node count grows
  • Deep familiarity with GPU or accelerator performance concepts - memory bandwidth, kernel launch overhead, occupancy, quantization, collective communication
  • Experience with high-throughput or batch inference systems (NVIDIA Triton Inference Server, TensorRT, ONNX Runtime, Ray, or similar)
  • Fluency in Python and proficiency in C++ or another systems language
  • Excellent debugging, analytical, and problem-solving skills
  • A deep understanding of machine learning foundations, and the ability to develop technical solutions for problems with no established playbook

Skills

Required:Distributed multi-node trainingProfiling and roofline analysisGPU/accelerator performance conceptsHigh-throughput/batch inference systemsPythonC++ or systems language
Last updated Oct 18, 2026. Interested? Apply on the company site.

About Applied Intuition

Building vehicle intelligence | Applied Intuition

Applied Intuition is the vehicle intelligence company with the tools, Vehicle OS, and autonomy stacks for creating safe, AI-powered vehicle experiences.

AIAutonomy stacksSelf-Driving SystemSimulationTesting & ValidationVehicle IntelligenceVehicle OSai-infrastructure-and-development
Company profileWebsite

More open roles at Applied Intuition